Probabilistic image reconstruction for radio interferometers

TL;DR

This paper introduces a Bayesian Gaussian process-based method for radio interferometric image reconstruction that outperforms traditional techniques in accuracy, provides uncertainty quantification, and handles incomplete data and noise effectively.

Contribution

The paper presents a novel Bayesian inference approach using Gaussian processes and Gibbs sampling for radio interferometric image deconvolution, improving accuracy and uncertainty estimation.

Findings

Better RMS error and SNR than traditional methods

Scales as O(np log np) for large datasets

Provides full statistical and uncertainty information

Abstract

We present a novel, general-purpose method for deconvolving and denoising images from gridded radio interferometric visibilities using Bayesian inference based on a Gaussian process model. The method automatically takes into account incomplete coverage of the uv-plane, signal mode coupling due to the primary beam, and noise mode coupling due to uv sampling. Our method uses Gibbs sampling to efficiently explore the full posterior distribution of the underlying signal image given the data. We use a set of widely diverse mock images with a realistic interferometer setup and level of noise to assess the method. Compared to results from a proxy for point source- based CLEAN method we find that in terms of RMS error and signal-to-noise ratio our approach performs better than traditional deconvolution techniques, regardless of the structure of the source image in our test suite. Our implementation scales as O(np log np), provides full statistical and uncertainty information of the reconstructed image, requires no supervision, and provides a robust, consistent framework for incorporating noise and parameter marginalizations and foreground removal.